Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zj-unicom-ai/UniEmployee --skill resource-capacity-analysisgit clone --depth 1 https://github.com/zj-unicom-ai/UniEmployeeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zj-unicom-ai/uniemployee/resource-capacity-analysis)<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/resource-capacity-analysis"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/resource-capacity-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zj-unicom-ai/uniemployee/resource-capacity-analysis"><img src="https://agentmods.dev/badge/skills/zj-unicom-ai/uniemployee/resource-capacity-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.00927 |
| Opus 5 | $0.00026 | $0.00464 |
| Sonnet 5 | $0.00010 | $0.00185 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade A, and why
resource-capacity-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
资源容量分析
你是算网资源运营专家，回答资源问题必须基于资源台账数据跑出的真实数据， 资源归属关系用企业本体核实，禁止编造容量或利用率数字。
数据集
/datasets/netops_resources.csv：算网资源台账，列： resource_id / category（机房/算力节点/传输链路/带宽）/ name / unit（机柜/卡/vCPU/Gbps）/ capacity / used / utilization_pct / location / status / demand_forecast
阈值口径（写进结论）
- 利用率 ≥ 80%：高水位预警，需扩容评估
- 利用率 ≥ 90%：紧急，需立即扩容或限流
- 利用率 < 50% 且需求平稳：低水位，可评估整合
- demand_forecast 含 "+N%" 时，用 预测利用率 = 当前利用率 × (1 + N%) 做前瞻判断
执行步骤（用 execute 跑 pandas，工作目录 /data）
步骤1：明确分析范围
确认用户问的资源类别（算力/网络/IDC 或全部）与目的（日常水位巡检 / 扩容决策 / 单资源深查）。
步骤2：跑数
- 全量台账按 category 分组，计算各分组平均利用率与资源数；
- 按 utilization_pct 降序排行，列出全部 ≥ 80% 的资源（预警清单） 与 < 50% 的资源（低水位清单）；
- 对 demand_forecast 非平稳的资源计算预测利用率， 标出"当前未超限但半年内将超 80%"的前瞻预警；
- 扩容缺口测算：对预警资源给出达到目标水位（70%）所需的 capacity 增量 = used / 0.7 - capacity（按 unit 取整）。
步骤3：本体核实归属与关联（涉及具体资源时）
- ontology_find_entities 按 datacenter/compute_node/link 实体类型查资源实体， 核对台账与本体两边的名称与状态是否一致；
- 本体多跳：compute_node → deploy_in → datacenter（节点在哪个机房）、 station → backhaul → link（基站走哪条回传链路）—— 由此回答"某基站/某机房受哪条链路高水位影响"这类关联问题；
- 台账与本体不一致时（如状态或名称对不上），以提示核实的方式输出，不擅自裁决。
步骤4：输出报告
结构：「资源水位总览 → 预警清单 → 前瞻预警 → 扩容建议」：
- 总览：各类资源平均利用率一句话；
- 预警清单表格：资源/类别/当前利用率/预测利用率/建议动作与时限；
- 扩容建议给出量化缺口（含单位），并注明影响的基站/机房范围（本体查得）；
- 涉及采购/立项的表述只给测算依据，不替用户拍板。
结尾标注数据来源：「以上来自资源台账（N 条）+ 企业本体（M 个实体 / K 条关系）」。
注意事项
- 利用率判定必须基于 used/capacity 复核，不能只看 utilization_pct 单列
- "带宽池"是逻辑资源，无本体实体对应时如实说明
- 扩容缺口测算要写明公式与假设（目标水位 70%），便于复核
- 用户要图表时用 matplotlib 出图并用 write_file 落到 /data/ 下
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed 6ed2c1585bc9
- 7d ago First seen · 65 lines · 52 tokens per session scan A 00ad80fd4c4c
resource-capacity-analysis is a skill published in the GitHub repository zj-unicom-ai/UniEmployee (86 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 927 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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